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In order to process dimensional data, it must be obtained from the source and assembled into a dimensional format as required by the target table s design. These activities are represented by the first three steps in Figure 17-2. Step 1.1 calls for the acquisition of the source data needed to load the dimension. In a Corporate Information Factory, most dimension tables will have a single source: the enterprise data warehouse. In other architectures, it is possible for a dimension table to have multiple sources. A dimension table with multiple sources may require multiple extraction processes, with additional processing to consolidate the information extracted

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into a single pool of records for processing Or, separate load processes may be designed for each of the sources Source data may be acquired through a number of means For example, mainframe data may be extracted programmatically into a file or through specialized utilities Relational data may be extracted directly via SQL or provided through an export file Real-time data may arrive through a messaging system or a web service External data may be supplied online or through batch extracts If the source data is relational, distinct values should be queried to avoid unnecessary processing If it is nonrelational, developers may choose to bulk-load it into a relational staging area so SQL operations can be used in this initial step Once extracted, source data often needs to be reorganized so it can be processed one row at a time This is represented by step 12.

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Source data may need to be pivoted or transposed, so that all the elements that constitute a dimension record are in a single row In many cases, little work needs to be done, but in other cases this step may call for intensive processing Hierarchical data structures may require flattening, denormalized data structures may require pivoting, and so forth Once the source data is captured and restructured as a series of rows, step 13 takes each row of source data and assembles the dimensional attributes This is a matter of applying transformation rules as spelled out in the design documentation Codes may be decoded into descriptive values Fields with multi-part attributes may be split into a series of attributes Sets of fields may be concatenated NULL field values may be replaced with more understandable text such as unavailable or unknown.

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The case and format of attributes may be adjusted, and so forth This task might also be broken down to include data cleansing tasks, such as name and address standardization In simple cases, it may be possible to execute tasks 11 through 13 with a single SQL statement; other cases may be significantly more complex Once these first three tasks are completed, one or more potential dimension records are ready for further processing The next several tasks will determine how each record is handled New records, type 1 changes, and type 2 changes must all be processed Some records may include both type 1 and type 2 changes..

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New records are those that have a natural key that has not been previously encountered in the star. When a new record is identified, it must be assigned a surrogate key and inserted into the dimension table. In Figure 17-2, these records are identified and processed in steps 1.4, 1.9, and 1.10. To determine whether an incoming record is new, step 1.4 searches for its natural key in the dimension table. This check is often referred to as a lookup. If the natural key value is not found in the existing table, the record is new. Step 1.9 assigns the next available surrogate key to the record, and step 1.10 loads it into the dimension table. If, on the other hand, a record is found during step 1.4, then the natural key is already present in the dimension. The record must be further examined to determine if it has undergone type 1 or type 2 changes. As previously mentioned, a changed data identification system may obviate the need to look for new records after extracting data. These solutions monitor the source directly, identifying new and changed data prior to extraction. The same requirements must be met, however: it is necessary to extract the source data and process the new records.

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